| sfHSD | R Documentation |
The function sfHSD implements a Hwang-Shih-DeCani spending function.
This is the default spending function for gsDesign(). Normally it
will be passed to gsDesign in the parameter sfu for the upper
bound or sfl for the lower bound to specify a spending function
family for a design. In this case, the user does not need to know the
calling sequence. The calling sequence is useful, however, when the user
wishes to plot a spending function as demonstrated below in examples.
A Hwang-Shih-DeCani spending function takes the form
f(t;\alpha,
\gamma)=\alpha(1-e^{-\gamma t})/(1-e^{-\gamma})
where \gamma is the
value passed in param. A value of \gamma=-4 is used
to approximate an O'Brien-Fleming design (see sfExponential
for a better fit), while a value of \gamma=1 approximates a
Pocock design well.
sfHSD(alpha, t, param)
alpha |
Real value |
t |
A vector of points with increasing values from 0 to 1, inclusive. Values of the proportion of sample size/information for which the spending function will be computed. |
param |
A single real value specifying the gamma parameter for which Hwang-Shih-DeCani spending is to be computed; allowable range is [-40, 40] |
An object of type spendfn. See vignette("SpendingFunctionOverview") for further details.
The gsDesign technical manual is available at https://keaven.github.io/gsd-tech-manual/.
Keaven Anderson keaven_anderson@merck.com
Jennison C and Turnbull BW (2000), Group Sequential Methods with Applications to Clinical Trials. Boca Raton: Chapman and Hall.
vignette("SpendingFunctionOverview"), gsDesign,
vignette("gsDesignPackageOverview")
library(ggplot2)
# design a 4-analysis trial using a Hwang-Shih-DeCani spending function
# for both lower and upper bounds
x <- gsDesign(k = 4, sfu = sfHSD, sfupar = -2, sfl = sfHSD, sflpar = 1)
# print the design
x
# since sfHSD is the default for both sfu and sfl,
# this could have been written as
x <- gsDesign(k = 4, sfupar = -2, sflpar = 1)
# print again
x
# plot the spending function using many points to obtain a smooth curve
# show default values of gamma to see how the spending function changes
# also show gamma=1 which is supposed to approximate a Pocock design
t <- 0:100 / 100
plot(t, sfHSD(0.025, t, -4)$spend,
xlab = "Proportion of final sample size",
ylab = "Cumulative Type I error spending",
main = "Hwang-Shih-DeCani Spending Function Example", type = "l"
)
lines(t, sfHSD(0.025, t, -2)$spend, lty = 2)
lines(t, sfHSD(0.025, t, 1)$spend, lty = 3)
legend(
x = c(.0, .375), y = .025 * c(.8, 1), lty = 1:3,
legend = c("gamma= -4", "gamma= -2", "gamma= 1")
)
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